ORCID Profile
0000-0003-0239-6785
Current Organisation
Kyungpook National University
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Publisher: Elsevier BV
Date: 11-2021
Publisher: Springer Singapore
Date: 2020
Publisher: Springer International Publishing
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 26-11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: Association for Computing Machinery (ACM)
Date: 30-04-2019
DOI: 10.1145/3309665
Abstract: In recent years, visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real-world problems such as human-computer interaction, autonomous vehicles, robotics, surveillance, and security just to name a few. In the current study, we review latest trends and advances in the tracking area and evaluate the robustness of different trackers based on the feature extraction methods. The first part of this work includes a comprehensive survey of the recently proposed trackers. We broadly categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs. Each category is further classified into various types based on the architecture and the tracking mechanism. In the second part of this work, we experimentally evaluated 24 recent trackers for robustness and compared handcrafted and deep feature based trackers. We observe that trackers using deep features performed better, though in some cases a fusion of both increased performance significantly. To overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms. We analyze the performance of trackers over 11 different challenges in OTTC and 3 other benchmarks. Our study concludes that Discriminative Correlation Filter (DCF) based trackers perform better than the others. Our study also reveals that inclusion of different types of regularizations over DCF often results in boosted tracking performance. Finally, we sum up our study by pointing out some insights and indicating future trends in the visual object tracking field.
Publisher: IEEE
Date: 10-2019
Publisher: Springer Singapore
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: ACM
Date: 08-04-2019
Publisher: MDPI AG
Date: 06-07-2020
DOI: 10.3390/S20133780
Abstract: CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.
Publisher: IEEE
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
No related grants have been discovered for Soon Ki Jung.